The modes \(M\)  as well as the test sequences are selected as described in  [1]}. The policy for all methods is parameterized by a Transformer [2]} with 3 layers, dimension 64, and 8 attention heads. All methods are trained for 50,000 iterations with minibatch size of 16 using Adam optimizer. For GFlowNets with FM objective as well as the baselines, we use the exact same implementation and hyperparameters reported in [1]}. For TB and SubTB(\(\lambda \) ), we pick the best learning rate from \(\lbrace 0.0075, 0.001, 0.001, 0.003, 0.005\rbrace \)  for forward logits, and for Z, use a learning rate of \(10\times \)  the learning rate for forward logits. For SubTB(\(\lambda \) ), we found the best \(\lambda \)  value of 1.9 from the values \(\lbrace 0.8, 0.9, 1.1, 1.3, 1.5, 1.7, 1.9, 2.0\rbrace \) .
